Abstract
Background: Previous research has shown a positive information gain when using word embeddings from learner corpus data for metaphor classification in a neural network (Stemle & Onysko, 2018).
Aim: Explore the potential influence of the data structure in the annotated part of the ETS Corpus of Non-Native written English; particular focus on: proficiency ratings, essay prompt and L1 of the learner.
System: fastText word embeddings from different corpora in a bi-directional recursive neural network with long-term short-term memory (LSTM BiRNN); a flat sequence to sequence neural network with one hidden layer using TensorFlow+Keras (Abadi et al., 2015) in Python.